Recent research has sought to develop principles and applications of human-aided and, more generally, mixed-initiative problem solving, where complementary contributions from people and computing systems are combined to generate solutions. The ideal mix of human and computer contributions changes as the competencies of computational systems grow and with the development of new ways for people and machines to collaborate, with sequential or parallel efforts. People are known to perform better than the best available computational algorithms for high-level reasoning, decision making, and recognition, especially when these tasks rely on a deep fund of commonsense knowledge.
Visual category recognition is a particularly challenging problem and techniques based on computer vision often require human involvement to learn good object category models. The most basic level of human involvement is providing labeled data that the system can use to learn visual categories. Since this labeling process is often very expensive, much recent work has focused on ways to reduce the number of labeled examples required to learn accurate models. These systems aim to maximally utilize the human effort involved in labeling examples. Other solutions for addressing the labeling problem include embedding the labeling task in popular games, and asking users to provide finer-grained information by selecting and labeling specific objects within images. Implementing such a method, however, is costly and still requires active participation from a user.
Accordingly, there is a need for an efficient visual object categorization framework that fuses and identifies the respective strengths of computer and human processing, while minimizing the amount of human involvement required. The above-described deficiencies of current techniques are merely intended to provide an overview of some of the problems of conventional systems, and are not intended to be exhaustive. Other problems with conventional systems and corresponding benefits of the various non-limiting embodiments described herein may become further apparent upon review of the following description.